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Constructing Daily Economic Sentiment Indices Based on Google Trends

Vera Eichenauer, Ronald Indergand (), Isabel Z. Martínez () and Christoph Sax ()
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Ronald Indergand: State Secretariat for Economic Affairs SECO, Switzerland
Isabel Z. Martínez: KOF Swiss Economic Institute, ETH Zurich, Switzerland
Christoph Sax: University of Basel; cynkra LLC, Switzerland

Authors registered in the RePEc Author Service: Isabel Martínez ()

No 20-484, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

Abstract: Google Trends have become a popular data source for social science research. We show that for small countries or sub-national regions like U.S. states, underlying sampling noise in Google Trends can be substantial. The data may therefore be unreliable for time series analysis and is furthermore frequency-inconsistent: daily data differs from weekly or monthly data. We provide a novel sampling technique along with the R-package trendecon in order to generate stable daily Google search results that are consistent with weekly and monthly queries of Google Trends. We use this new approach to construct long and consistent daily economic indices for the (mainly) German-speaking countries Germany, Austria, and Switzerland. The resulting indices are significantly correlated with traditional leading indicators, with the advantage that they are available much earlier.

Keywords: Google Trends; measurement; high frequency; forecasting; Covid-19 Market; Euro; sectoral heterogeneity (search for similar items in EconPapers)
JEL-codes: E01 E32 E37 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2020-06
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mac
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Handle: RePEc:kof:wpskof:20-484